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data-structure-typed

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Installation and Usage

npm

npm i data-structure-typed --save

yarn

yarn add data-structure-typed
import {
  Heap, Graph, Queue, Deque, PriorityQueue, BST, Trie, DoublyLinkedList,
  AVLTree, SinglyLinkedList, DirectedGraph, RedBlackTree, TreeMultiMap,
  DirectedVertex, Stack, AVLTreeNode
} from 'data-structure-typed';

If you only want to use a specific data structure independently, you can install it separately, for example, by running

npm i heap-typed --save

Why

Do you envy C++ with STL (std::), Python with collections, and Java with java.util ? Well, no need to envy anymore! JavaScript and TypeScript now have data-structure-typed.Benchmark compared with C++ STL. API standards aligned with ES6 and Java. Usability is comparable to Python

Performance

Performance surpasses that of native JS/TS

Method Time Taken Data Scale Belongs To big O
Queue.push & shift 5.83 ms 100K Ours O(1)
Array.push & shift 2829.59 ms 100K Native JS O(n)
Deque.unshift & shift 2.44 ms 100K Ours O(1)
Array.unshift & shift 4750.37 ms 100K Native JS O(n)
HashMap.set 122.51 ms 1M Ours O(1)
Map.set 223.80 ms 1M Native JS O(1)
Set.add 185.06 ms 1M Native JS O(1)

Conciseness and uniformity

In java.utils, you need to memorize a table for all sequential data structures(Queue, Deque, LinkedList),

Java ArrayList Java Queue Java ArrayDeque Java LinkedList
add offer push push
remove poll removeLast removeLast
remove poll removeFirst removeFirst
add(0, element) offerFirst unshift unshift

whereas in our data-structure-typed, you only need to remember four methods: push, pop, shift, and unshift for all sequential data structures(Queue, Deque, DoublyLinkedList, SinglyLinkedList and Array).

Data structures available

We provide data structures that are not available in JS/TS

Data Structure Unit Test Perf Test API Doc NPM Downloads
Binary Tree Docs NPM NPM Downloads
Binary Search Tree (BST) Docs NPM NPM Downloads
AVL Tree Docs NPM NPM Downloads
Red Black Tree Docs NPM NPM Downloads
Tree Multimap Docs NPM NPM Downloads
Heap Docs NPM NPM Downloads
Priority Queue Docs NPM NPM Downloads
Max Priority Queue Docs NPM NPM Downloads
Min Priority Queue Docs NPM NPM Downloads
Trie Docs NPM NPM Downloads
Graph Docs NPM NPM Downloads
Directed Graph Docs NPM NPM Downloads
Undirected Graph Docs NPM NPM Downloads
Queue Docs NPM NPM Downloads
Deque Docs NPM NPM Downloads
Hash Map Docs
Linked List Docs NPM NPM Downloads
Singly Linked List Docs NPM NPM Downloads
Doubly Linked List Docs NPM NPM Downloads
Stack Docs NPM NPM Downloads
Segment Tree Docs
Binary Indexed Tree Docs

Vivid Examples

AVL Tree

Try it out, or you can run your own code using our visual tool

Tree Multi Map

Try it out

Directed Graph

Try it out

Map Graph

Try it out

Code Snippets

Red Black Tree snippet

TS

import { RedBlackTree } from 'data-structure-typed';

const rbTree = new RedBlackTree<number>();
rbTree.addMany([11, 3, 15, 1, 8, 13, 16, 2, 6, 9, 12, 14, 4, 7, 10, 5])
rbTree.isAVLBalanced();    // true
rbTree.delete(10);
rbTree.isAVLBalanced();    // true
rbTree.print()
//         ___6________
//        /            \
//      ___4_       ___11________
//     /     \     /             \
//    _2_    5    _8_       ____14__
//   /   \       /   \     /        \
//   1   3       7   9    12__     15__
//                            \        \
//                           13       16

JS

import { RedBlackTree } from 'data-structure-typed';

const rbTree = new RedBlackTree();
rbTree.addMany([11, 3, 15, 1, 8, 13, 16, 2, 6, 9, 12, 14, 4, 7, 10, 5])
rbTree.isAVLBalanced();    // true
rbTree.delete(10);
rbTree.isAVLBalanced();    // true
rbTree.print()
//         ___6________
//        /            \
//      ___4_       ___11________
//     /     \     /             \
//    _2_    5    _8_       ____14__
//   /   \       /   \     /        \
//   1   3       7   9    12__     15__
//                            \        \
//                           13       16

Free conversion between data structures.

const orgArr = [6, 1, 2, 7, 5, 3, 4, 9, 8];
const orgStrArr = ["trie", "trial", "trick", "trip", "tree", "trend", "triangle", "track", "trace", "transmit"];
const entries = [[6, "6"], [1, "1"], [2, "2"], [7, "7"], [5, "5"], [3, "3"], [4, "4"], [9, "9"], [8, "8"]];

const queue = new Queue(orgArr);
queue.print();
// [6, 1, 2, 7, 5, 3, 4, 9, 8]

const deque = new Deque(orgArr);
deque.print();
// [6, 1, 2, 7, 5, 3, 4, 9, 8]

const sList = new SinglyLinkedList(orgArr);
sList.print();
// [6, 1, 2, 7, 5, 3, 4, 9, 8]

const dList = new DoublyLinkedList(orgArr);
dList.print();
// [6, 1, 2, 7, 5, 3, 4, 9, 8]

const stack = new Stack(orgArr);
stack.print();
// [6, 1, 2, 7, 5, 3, 4, 9, 8]

const minHeap = new MinHeap(orgArr);
minHeap.print();
// [1, 5, 2, 7, 6, 3, 4, 9, 8]

const maxPQ = new MaxPriorityQueue(orgArr);
maxPQ.print();
// [9, 8, 4, 7, 5, 2, 3, 1, 6]

const biTree = new BinaryTree(entries);
biTree.print();
//         ___6___
//        /       \
//     ___1_     _2_
//    /     \   /   \
//   _7_    5   3   4
//  /   \
//  9   8

const bst = new BST(entries);
bst.print();
//     _____5___
//    /         \
//   _2_       _7_
//  /   \     /   \
//  1   3_    6   8_
//        \         \
//        4         9


const rbTree = new RedBlackTree(entries);
rbTree.print();
//     ___4___
//    /       \
//   _2_     _6___
//  /   \   /     \
//  1   3   5    _8_
//              /   \
//              7   9


const avl = new AVLTree(entries);
avl.print();
//     ___4___
//    /       \
//   _2_     _6___
//  /   \   /     \
//  1   3   5    _8_
//              /   \
//              7   9

const treeMulti = new TreeMultiMap(entries);
treeMulti.print();
//     ___4___
//    /       \
//   _2_     _6___
//  /   \   /     \
//  1   3   5    _8_
//              /   \
//              7   9

const hm = new HashMap(entries);
hm.print()
// [[6, "6"], [1, "1"], [2, "2"], [7, "7"], [5, "5"], [3, "3"], [4, "4"], [9, "9"], [8, "8"]]

const rbTreeH = new RedBlackTree(hm);
rbTreeH.print();
//     ___4___
//    /       \
//   _2_     _6___
//  /   \   /     \
//  1   3   5    _8_
//              /   \
//              7   9

const pq = new MinPriorityQueue(orgArr);
pq.print();
// [1, 5, 2, 7, 6, 3, 4, 9, 8]

const bst1 = new BST(pq);
bst1.print();
//     _____5___
//    /         \
//   _2_       _7_
//  /   \     /   \
//  1   3_    6   8_
//        \         \
//        4         9

const dq1 = new Deque(orgArr);
dq1.print();
// [6, 1, 2, 7, 5, 3, 4, 9, 8]
const rbTree1 = new RedBlackTree(dq1);
rbTree1.print();
//    _____5___
//   /         \
//  _2___     _7___
// /     \   /     \
// 1    _4   6    _9
//      /         /
//      3         8


const trie2 = new Trie(orgStrArr);
trie2.print();
// ['trie', 'trial', 'triangle', 'trick', 'trip', 'tree', 'trend', 'track', 'trace', 'transmit']
const heap2 = new Heap(trie2, { comparator: (a, b) => Number(a) - Number(b) });
heap2.print();
// ['transmit', 'trace', 'tree', 'trend', 'track', 'trial', 'trip', 'trie', 'trick', 'triangle']
const dq2 = new Deque(heap2);
dq2.print();
// ['transmit', 'trace', 'tree', 'trend', 'track', 'trial', 'trip', 'trie', 'trick', 'triangle']
const entries2 = dq2.map((el, i) => [i, el]);
const avl2 = new AVLTree(entries2);
avl2.print();
//     ___3_______
//    /           \
//   _1_       ___7_
//  /   \     /     \
//  0   2    _5_    8_
//          /   \     \
//          4   6     9

Binary Search Tree (BST) snippet

import { BST, BSTNode } from 'data-structure-typed';

const bst = new BST<number>();
bst.add(11);
bst.add(3);
bst.addMany([15, 1, 8, 13, 16, 2, 6, 9, 12, 14, 4, 7, 10, 5]);
bst.size === 16;                // true
bst.has(6);                     // true
const node6 = bst.getNode(6);   // BSTNode
bst.getHeight(6) === 2;         // true
bst.getHeight() === 5;          // true
bst.getDepth(6) === 3;          // true

bst.getLeftMost()?.key === 1;   // true

bst.delete(6);
bst.get(6);                     // undefined
bst.isAVLBalanced();            // true
bst.bfs()[0] === 11;            // true
bst.print()
//       ______________11_____           
//      /                     \          
//   ___3_______            _13_____
//  /           \          /        \    
//  1_     _____8____     12      _15__
//    \   /          \           /     \ 
//    2   4_       _10          14    16
//          \     /                      
//          5_    9
//            \                          
//            7

const objBST = new BST<number, { height: number, age: number }>();

objBST.add(11, { "name": "Pablo", "age": 15 });
objBST.add(3, { "name": "Kirk", "age": 1 });

objBST.addMany([15, 1, 8, 13, 16, 2, 6, 9, 12, 14, 4, 7, 10, 5], [
    { "name": "Alice", "age": 15 },
    { "name": "Bob", "age": 1 },
    { "name": "Charlie", "age": 8 },
    { "name": "David", "age": 13 },
    { "name": "Emma", "age": 16 },
    { "name": "Frank", "age": 2 },
    { "name": "Grace", "age": 6 },
    { "name": "Hannah", "age": 9 },
    { "name": "Isaac", "age": 12 },
    { "name": "Jack", "age": 14 },
    { "name": "Katie", "age": 4 },
    { "name": "Liam", "age": 7 },
    { "name": "Mia", "age": 10 },
    { "name": "Noah", "age": 5 }
  ]
);

objBST.delete(11);

AVLTree snippet

import { AVLTree } from 'data-structure-typed';

const avlTree = new AVLTree<number>();
avlTree.addMany([11, 3, 15, 1, 8, 13, 16, 2, 6, 9, 12, 14, 4, 7, 10, 5])
avlTree.isAVLBalanced();    // true
avlTree.delete(10);
avlTree.isAVLBalanced();    // true

Directed Graph simple snippet

import { DirectedGraph } from 'data-structure-typed';

const graph = new DirectedGraph<string>();

graph.addVertex('A');
graph.addVertex('B');

graph.hasVertex('A');       // true
graph.hasVertex('B');       // true
graph.hasVertex('C');       // false

graph.addEdge('A', 'B');
graph.hasEdge('A', 'B');    // true
graph.hasEdge('B', 'A');    // false

graph.deleteEdgeSrcToDest('A', 'B');
graph.hasEdge('A', 'B');    // false

graph.addVertex('C');

graph.addEdge('A', 'B');
graph.addEdge('B', 'C');

const topologicalOrderKeys = graph.topologicalSort(); // ['A', 'B', 'C']

Undirected Graph snippet

import { UndirectedGraph } from 'data-structure-typed';

const graph = new UndirectedGraph<string>();
graph.addVertex('A');
graph.addVertex('B');
graph.addVertex('C');
graph.addVertex('D');
graph.deleteVertex('C');
graph.addEdge('A', 'B');
graph.addEdge('B', 'D');

const dijkstraResult = graph.dijkstra('A');
Array.from(dijkstraResult?.seen ?? []).map(vertex => vertex.key) // ['A', 'B', 'D']

API docs & Examples

API Docs

Live Examples

Examples Repository

Benchmark

MacBook Pro (15-inch, 2018)

Processor 2.2 GHz 6-Core Intel Core i7

Memory 16 GB 2400 MHz DDR4

Graphics Radeon Pro 555X 4 GB

Intel UHD Graphics 630 1536 MB

macOS Big Sur

Version 11.7.9

heap
test name time taken (ms) executions per sec sample deviation
100,000 add 6.09 164.12 1.35e-4
100,000 add & poll 34.55 28.94 6.43e-4
rb-tree
test name time taken (ms) executions per sec sample deviation
100,000 add 76.73 13.03 0.00
100,000 add randomly 80.67 12.40 0.00
100,000 get 110.86 9.02 0.00
100,000 iterator 24.99 40.02 0.00
100,000 add & delete orderly 152.66 6.55 0.00
100,000 add & delete randomly 230.75 4.33 0.00
queue
test name time taken (ms) executions per sec sample deviation
1,000,000 push 39.27 25.46 0.01
100,000 push & shift 4.53 220.81 4.84e-4
Native JS Array 100,000 push & shift 1948.05 0.51 0.02
deque
test name time taken (ms) executions per sec sample deviation
1,000,000 push 23.22 43.06 0.00
1,000,000 push & pop 29.68 33.69 0.00
1,000,000 push & shift 29.33 34.09 0.00
100,000 push & shift 3.10 323.01 2.47e-4
Native JS Array 100,000 push & shift 1942.12 0.51 0.02
100,000 unshift & shift 2.77 360.50 2.43e-4
Native JS Array 100,000 unshift & shift 3835.21 0.26 0.03
hash-map
test name time taken (ms) executions per sec sample deviation
1,000,000 set 112.38 8.90 0.02
Native JS Map 1,000,000 set 199.97 5.00 0.01
Native JS Set 1,000,000 add 163.34 6.12 0.01
1,000,000 set & get 109.86 9.10 0.02
Native JS Map 1,000,000 set & get 255.33 3.92 0.00
Native JS Set 1,000,000 add & has 163.91 6.10 0.00
1,000,000 ObjKey set & get 317.89 3.15 0.04
Native JS Map 1,000,000 ObjKey set & get 282.99 3.53 0.03
Native JS Set 1,000,000 ObjKey add & has 253.93 3.94 0.03
trie
test name time taken (ms) executions per sec sample deviation
100,000 push 43.71 22.88 7.33e-4
100,000 getWords 83.63 11.96 0.00
avl-tree
test name time taken (ms) executions per sec sample deviation
100,000 add 271.93 3.68 0.01
100,000 add randomly 318.27 3.14 0.00
100,000 get 128.85 7.76 0.00
100,000 iterator 29.09 34.38 0.00
100,000 add & delete orderly 435.48 2.30 7.44e-4
100,000 add & delete randomly 578.70 1.73 0.00
binary-tree-overall
test name time taken (ms) executions per sec sample deviation
10,000 RBTree add randomly 6.69 149.54 1.06e-4
10,000 RBTree get randomly 9.19 108.82 1.43e-4
10,000 RBTree add & delete randomly 18.54 53.94 1.73e-4
10,000 AVLTree add randomly 23.70 42.20 1.88e-4
10,000 AVLTree get randomly 9.89 101.11 0.00
10,000 AVLTree add & delete randomly 44.44 22.50 4.30e-4
directed-graph
test name time taken (ms) executions per sec sample deviation
1,000 addVertex 0.10 9766.65 9.83e-7
1,000 addEdge 6.15 162.57 7.99e-4
1,000 getVertex 0.05 2.18e+4 4.52e-7
1,000 getEdge 22.70 44.06 0.00
tarjan 203.00 4.93 0.01
topologicalSort 176.40 5.67 0.00
doubly-linked-list
test name time taken (ms) executions per sec sample deviation
1,000,000 push 222.02 4.50 0.07
1,000,000 unshift 220.41 4.54 0.05
1,000,000 unshift & shift 185.31 5.40 0.01
1,000,000 addBefore 317.20 3.15 0.07
singly-linked-list
test name time taken (ms) executions per sec sample deviation
1,000,000 push & shift 204.82 4.88 0.09
10,000 push & pop 221.88 4.51 0.03
10,000 addBefore 247.28 4.04 0.01
priority-queue
test name time taken (ms) executions per sec sample deviation
100,000 add 26.97 37.08 7.97e-4
100,000 add & poll 74.55 13.41 5.19e-4
stack
test name time taken (ms) executions per sec sample deviation
1,000,000 push 35.54 28.14 0.00
1,000,000 push & pop 44.89 22.27 0.01

The corresponding relationships between data structures in different language standard libraries.

Data Structure Typed C++ STL java.util Python collections
Heap<E> - - heapq
PriorityQueue<E> priority_queue<T> PriorityQueue<E> -
Deque<E> deque<T> ArrayDeque<E> deque
Queue<E> queue<T> Queue<E> -
HashMap<K, V> unordered_map<K, V> HashMap<K, V> defaultdict
DoublyLinkedList<E> list<T> LinkedList<E> -
SinglyLinkedList<E> - - -
BinaryTree<K, V> - - -
BST<K, V> - - -
RedBlackTree<E> set<T> TreeSet<E> -
RedBlackTree<K, V> map<K, V> TreeMap<K, V> -
TreeMultiMap<K, V> multimap<K, V> - -
TreeMultiMap<E> multiset<T> - -
Trie - - -
DirectedGraph<V, E> - - -
UndirectedGraph<V, E> - - -
PriorityQueue<E> priority_queue<T> PriorityQueue<E> -
Array<E> vector<T> ArrayList<E> list
Stack<E> stack<T> Stack<E> -
HashMap<E> unordered_set<T> HashSet<E> set
- unordered_multiset - Counter
LinkedHashMap<K, V> - LinkedHashMap<K, V> OrderedDict
- unordered_multimap<K, V> - -
- bitset<N> - -

Built-in classic algorithms

Algorithm Function Description Iteration Type
Binary Tree DFS Traverse a binary tree in a depth-first manner, starting from the root node, first visiting the left subtree, and then the right subtree, using recursion. Recursion + Iteration
Binary Tree BFS Traverse a binary tree in a breadth-first manner, starting from the root node, visiting nodes level by level from left to right. Iteration
Graph DFS Traverse a graph in a depth-first manner, starting from a given node, exploring along one path as deeply as possible, and backtracking to explore other paths. Used for finding connected components, paths, etc. Recursion + Iteration
Binary Tree Morris Morris traversal is an in-order traversal algorithm for binary trees with O(1) space complexity. It allows tree traversal without additional stack or recursion. Iteration
Graph BFS Traverse a graph in a breadth-first manner, starting from a given node, first visiting nodes directly connected to the starting node, and then expanding level by level. Used for finding shortest paths, etc. Recursion + Iteration
Graph Tarjan's Algorithm Find strongly connected components in a graph, typically implemented using depth-first search. Recursion
Graph Bellman-Ford Algorithm Finding the shortest paths from a single source, can handle negative weight edges Iteration
Graph Dijkstra's Algorithm Finding the shortest paths from a single source, cannot handle negative weight edges Iteration
Graph Floyd-Warshall Algorithm Finding the shortest paths between all pairs of nodes Iteration
Graph getCycles Find all cycles in a graph or detect the presence of cycles. Recursion
Graph getCutVertices Find cut vertices in a graph, which are nodes that, when removed, increase the number of connected components in the graph. Recursion
Graph getSCCs Find strongly connected components in a graph, which are subgraphs where any two nodes can reach each other. Recursion
Graph getBridges Find bridges in a graph, which are edges that, when removed, increase the number of connected components in the graph. Recursion
Graph topologicalSort Perform topological sorting on a directed acyclic graph (DAG) to find a linear order of nodes such that all directed edges go from earlier nodes to later nodes. Recursion

Software Engineering Design Standards

We strictly adhere to computer science theory and software development standards. Our LinkedList is designed in the traditional sense of the LinkedList data structure, and we refrain from substituting it with a Deque solely for the purpose of showcasing performance test data. However, we have also implemented a Deque based on a dynamic array concurrently.

Principle Description
Practicality Follows ES6 and ESNext standards, offering unified and considerate optional parameters, and simplifies method names.
Extensibility Adheres to OOP (Object-Oriented Programming) principles, allowing inheritance for all data structures.
Modularization Includes data structure modularization and independent NPM packages.
Efficiency All methods provide time and space complexity, comparable to native JS performance.
Maintainability Follows open-source community development standards, complete documentation, continuous integration, and adheres to TDD (Test-Driven Development) patterns.
Testability Automated and customized unit testing, performance testing, and integration testing.
Portability Plans for porting to Java, Python, and C++, currently achieved to 80%.
Reusability Fully decoupled, minimized side effects, and adheres to OOP.
Security Carefully designed security for member variables and methods. Read-write separation. Data structure software does not need to consider other security aspects.
Scalability Data structure software does not involve load issues.

supported module system

Now you can use it in Node.js and browser environments

CommonJS:require export.modules =

ESModule:   import export

Typescript:   import export

UMD:           var Deque = dataStructureTyped.Deque

CDN

Copy the line below into the head tag in an HTML document.

development

<script src='https://cdn.jsdelivr.net/npm/data-structure-typed/dist/umd/data-structure-typed.js'></script>

production

<script src='https://cdn.jsdelivr.net/npm/data-structure-typed/dist/umd/data-structure-typed.min.js'></script>

Copy the code below into the script tag of your HTML, and you're good to go with your development.

const { Heap } = dataStructureTyped;
const {
  BinaryTree, Graph, Queue, Stack, PriorityQueue, BST, Trie, DoublyLinkedList,
  AVLTree, MinHeap, SinglyLinkedList, DirectedGraph, TreeMultiMap,
  DirectedVertex, AVLTreeNode
} = dataStructureTyped;

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